Add "Open on run.dlang.io" badges

Author: wilzbach

README.md

@@ -23,46 +23,66 @@ Dlang core library for math, finance and a home for Dlang multidimensional array
 
 #### Example (3 sec)
 ```d
-import mir.ndslice;
-
-auto matrix = slice!double(3, 4);
-matrix[] = 0;
-matrix.diagonal[] = 1;
-
-auto row = matrix[2];
-row[3] = 6;
-assert(matrix[2, 3] == 6); // D & C index order
+/+dub.sdl:
+dependency "mir-algorithm" version="~>0.7.0"
++/
+
+void main()
+{
+    import mir.ndslice;
+
+    auto matrix = slice!double(3, 4);
+    matrix[] = 0;
+    matrix.diagonal[] = 1;
+
+    auto row = matrix[2];
+    row[3] = 6;
+    assert(matrix[2, 3] == 6); // D & C index order
+    
+    import std.stdio;
+    matrix.writeln; // [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 6]]
+}
 ```
 
+[![Open on run.dlang.io](https://img.shields.io/badge/run.dlang.io-open-blue.svg)](https://run.dlang.io/is/on6YTM)
+
 #### Example (30 sec)
 ```d
-import mir.ndslice;
-import std.stdio: writefln;
-
-enum fmt = "%(%(%.2f %)\n%)\n";
-
-// Magic sqaure. 
-// `a` is lazy, each element is computed on-demand.
-auto a = magic(5).as!float;
-writefln(fmt, a);
-
-// 5x5 grid on sqaure [1, 2] x [0, 1] with values x * x + y. 
-// `b` is lazy, each element is computed on-demand.
-auto b = linspace!float([5, 5], [1f, 2f], [0f, 1f]).map!"a * a + b";
-writefln(fmt, b);
-
-// allocate a 5 x 5 contiguous matrix
-auto c = slice!float(5, 5);
-
-c[] = transposed(a + b / 2); // no memory allocations here
+/+dub.sdl:
+dependency "mir-algorithm" version="~>0.7.0"
++/
+void main()
+{
+    import mir.ndslice;
+    import std.stdio : writefln;
+
+    enum fmt = "%(%(%.2f %)\n%)\n";
+
+    // Magic sqaure. 
+    // `a` is lazy, each element is computed on-demand.
+    auto a = magic(5).as!float;
+    writefln(fmt, a);
+
+    // 5x5 grid on sqaure [1, 2] x [0, 1] with values x * x + y. 
+    // `b` is lazy, each element is computed on-demand.
+    auto b = linspace!float([5, 5], [1f, 2f], [0f, 1f]).map!"a * a + b";
+    writefln(fmt, b);
+
+    // allocate a 5 x 5 contiguous matrix
+    auto c = slice!float(5, 5);
+
+    c[] = transposed(a + b / 2); // no memory allocations here
     // 1. b / 2 - lazy element-wise operation with scalars
     // 2. a + (...) - lazy element-wise operation with other slices
-        // Both slices must be `contiguous` or one-dimensional.
+    // Both slices must be `contiguous` or one-dimensional.
     // 3. transposed(...) - trasposes matrix view. The result is `universal` (numpy-like) matrix.
     // 5. c[] = (...) -- performs element-wise assignment.
-writefln(fmt, c);
+    writefln(fmt, c);
+}
 ```
 
+[![Open on run.dlang.io](https://img.shields.io/badge/run.dlang.io-open-blue.svg)](https://run.dlang.io/is/gSlbMb)
+
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